Session 3: Damage analysis (buildings and electric power facilities)

You will learn how to create earthquake and tornado hazard by using IN-CORE in this session

1. Fragility curve and Mapping

Damage analyses use fragility curve sets to calculate the limit state and damage stsate. Mapping then determines which fragility curve set applying to which inventory. For example, a building that has certain number of stories could be mapped to a specific set of fragility curves.

In IN-CORE, A user can both upload a set of fragility curves and mapping to the DFR3 services, or use local curve and mapping set objects.

This section briefly shows how to access fragility curves. A detailed instrcution of how to create your own fragility entities and mapping entities will be covered in next workshop.

User can easily browsing and searching Fragility Curves and Mappings via DFR3 Viewer

Analyses use DFR3 mapping sets from IN-CORE service to associate each element of a given set with one or more elements of a second set of DFR3 curves. In IN-CORE and pyIncore specifically, a building inventory (given set) is mapped to a (second) set of fragility curves.

Please note that in this notebook we use term function in programming context as a class function and curve as a function in mathematical context (lognormal curve).

# import pyIncore modules
from pyincore import FragilityService, IncoreClient, FragilityCurveSet, MappingSet

# Connect to IN-CORE service
client = IncoreClient()

# Initalize a FragilityService
fragility_services = FragilityService(client)

Connection successful to IN-CORE services. pyIncore version detected: 0.9.3

# Getting a fragility curve and metadata
# You can see the curve with this ID at DFR3 viewer
fragility_set_id = "5b47b2d7337d4a36187c61ce"

# Get local fragility curve set
fragility_curve_set = FragilityCurveSet(fragility_services.get_dfr3_set(fragility_set_id))
fragility_curve_set.fragility_curves

[<pyincore.models.periodstandardfragilitycurve.PeriodStandardFragilityCurve at 0x7fa3d9040e80>,
 <pyincore.models.periodstandardfragilitycurve.PeriodStandardFragilityCurve at 0x7fa3d90406d0>,
 <pyincore.models.periodstandardfragilitycurve.PeriodStandardFragilityCurve at 0x7fa3d9040730>]

# visualize this fragility curve using pyincore-viz package
from pyincore_viz.plotutil import PlotUtil as plot

plt = plot.get_fragility_plot(fragility_curve_set, title="Mid-Rise Steel Moment Frame")
plt.show()

2. Buiding Inventory Data

The schema (columns) of building Inventory has been going through several iteration (v5, v6, v7), and here is a list of supported column names in version 6. Having a fully populated Building Inventory, with no additional columns, will hold all the data needed to perform all analyses that are compatible with that version of the Building Inventory.

column name	description	type
GUID	added by IN-CORE	string
ARCHETYPE	building structure archetype	integer
OCC_TYPE	Broad HAZUS Occupancy Category (e.g. RES3 - multi-family residential)	string
APPR_BLDG	Appraised value for the building	double
SQ_FOOT	total building area in square feet	integer
DWELL_UNIT	total number of dwelling units in the building	integer
NO_STORIES	total number of stories for the building	integer
CONT_VAL	value of building contents	double
STR_TYP2	detailed structure type as per HAZUS MR-3 specifications	string
EFACILITY	essential facility designation	string
PAR_ID	parcel identifier	string
PARID_CARD	improvement identifier	string
BLDG_ID	building identifier (unique)	string
STR_PROB	probability that the structure type specified is correct	double
YEAR_BUILT	the year the structure was built	integer
A_STORIES	the number of above ground stories	integer
B_STORIES	the number of below ground stories	integer
BSMT_TYPE	the basement type	string
GSQ_FOOT	total ground floor area of the building in square feet	integer
OCC_DETAIL	“specific occupancy category, describing the detailed use of the building”	string
MAJOR_OCC	major occupancy category for the parcel in which the building is sited	string
BROAD_OCC	general occupancy categories	string
REPL_CST	replacement cost for the building from R.S. means square foot cost	double
STR_CST	structural component of the replacement cost	double
NSTRA_CST	acceleration sensitive component of replacement cost	double
NSTRD_CST	drift sensitive component of replacement cost	double
DGN_LVL	design level for the building as per HAZUS MR-3 specifications	string
OCC_TYP2	detailed HAZUS occupancy category for the building	string
TRACT_ID	census tract identifier	string

There are many ways to create building inventory dataset; for example 1) using GIS software (e.g. ArcGIS, QGIS, etc), python code.

In this exerercise, we will create a buiding inventory dataset in ESRI Shapefile from CSV file by using GeoPanda library.

Any analysis in pyIncore, by default uses Dataset Object as an input. Hence we have to convert building dataset into pyincore dataset object, in order to use it as an input for damage analysis. We can either upload the dataset to IN-CORE data services, using existing dataset id from the services, or using local files. The below introduces the basic concept of creating and using Dataset Object via either loading from local files, or connecting to remote IN-CORE Data Services.

2.1. Create a dataset from IN-CORE Data Service

A dataset can be created from data service with an id. pyIncore has utility methods to download the files and metadata from the data service to create a dataset obejct in local.

# Initialize a Data Serivce
from pyincore import DataService, Dataset

data_services = DataService(client)

# Loading dataset from Data Service
# See Data browser
building_dataset_id = "5a284f0bc7d30d13bc081a28"
buildings = Dataset.from_data_service(building_dataset_id, data_services)
buildings.metadata

Dataset already exists locally. Reading from local cached zip.
Unzipped folder found in the local cache. Reading from it...

{'id': '5a284f0bc7d30d13bc081a28',
 'deleted': False,
 'title': 'Hospitals',
 'description': '',
 'date': '2017-12-06T20:11:55+0000',
 'creator': 'ergo',
 'spaces': ['ergo'],
 'contributors': [],
 'fileDescriptors': [{'id': '5a284f0bc7d30d13bc081a2b',
   'deleted': False,
   'filename': 'all_bldgs_ver5_WGS1984.dbf',
   'mimeType': 'application/octet-stream',
   'size': 10859,
   'dataURL': '5a/28/5a284f0bc7d30d13bc081a2b/all_bldgs_ver5_WGS1984.dbf',
   'md5sum': '7ea0a4c769ca254a6b4821f2e737eb35'},
  {'id': '5a284f0bc7d30d13bc081a2e',
   'deleted': False,
   'filename': 'all_bldgs_ver5_WGS1984.fix',
   'mimeType': 'application/octet-stream',
   'size': 277,
   'dataURL': '5a/28/5a284f0bc7d30d13bc081a2e/all_bldgs_ver5_WGS1984.fix',
   'md5sum': '15c08cc1fac086265cb57ceac0785acb'},
  {'id': '5a284f0bc7d30d13bc081a31',
   'deleted': False,
   'filename': 'all_bldgs_ver5_WGS1984.prj',
   'mimeType': 'application/octet-stream',
   'size': 205,
   'dataURL': '5a/28/5a284f0bc7d30d13bc081a31/all_bldgs_ver5_WGS1984.prj',
   'md5sum': '30e5566d68356bfc059d296c42c0480e'},
  {'id': '5a284f0bc7d30d13bc081a34',
   'deleted': False,
   'filename': 'all_bldgs_ver5_WGS1984.qix',
   'mimeType': 'application/octet-stream',
   'size': 632,
   'dataURL': '5a/28/5a284f0bc7d30d13bc081a34/all_bldgs_ver5_WGS1984.qix',
   'md5sum': 'a1cba745192516c7e1cd0e4d4d13497c'},
  {'id': '5a284f0bc7d30d13bc081a37',
   'deleted': False,
   'filename': 'all_bldgs_ver5_WGS1984.shp',
   'mimeType': 'application/octet-stream',
   'size': 716,
   'dataURL': '5a/28/5a284f0bc7d30d13bc081a37/all_bldgs_ver5_WGS1984.shp',
   'md5sum': '6e1e96c4a6cf5762317054fe813d82bf'},
  {'id': '5a284f0bc7d30d13bc081a3a',
   'deleted': False,
   'filename': 'all_bldgs_ver5_WGS1984.shx',
   'mimeType': 'application/octet-stream',
   'size': 276,
   'dataURL': '5a/28/5a284f0bc7d30d13bc081a3a/all_bldgs_ver5_WGS1984.shx',
   'md5sum': '799965579a991f1f45afeb22c07c5ece'}],
 'dataType': 'ergo:buildingInventoryVer5',
 'storedUrl': '',
 'format': 'shapefile',
 'sourceDataset': '',
 'boundingBox': [-90.07376669874641,
  35.03298062856903,
  -89.71464767735003,
  35.207753220358086],
 'networkDataset': None}

2.2 Create a local dataset of building inventory

A dataset can be created from local files. We will create a buidling inventory data in ESRI shapefile from CSV file according to building inventory schema. With the files, we will create a local dataset object.

Step 1: Convert CSV file to ESRI Shapefile

Open CSV file in the file browser to see the contents.

# Load CSV file into GeoPandas, then export the GeoDataFrame to ESRI shapefile

import pandas as pd
import geopandas as gpd

df = pd.read_csv('ergo_memphis_hospitals.csv')

gdf = gpd.GeoDataFrame(df, crs='epsg:4326', geometry=gpd.points_from_xy(df.lon, df.lat))
gdf.to_file("sample_bldgs.shp")

Step 2: Add GUID

Each built-in infrastructure needs a GUID.

# pyincore has utility methods help user easily add GUID
from pyincore import GeoUtil

GeoUtil.add_guid("sample_bldgs.shp", "sample_bldgs_w_guid.shp")

True

Step 3: Create a local dataset for IN-CORE

The following code create a local Dataset object with ESRI shapefile for buiding inventory

local_buildings = Dataset.from_file("sample_bldgs_w_guid.shp", data_type="ergo:buildingInventoryVer7")

3. Building Damage Analysis

This analysis computes building damage based on a particular hazard. In this secion, you will perform building damage anlayis for both scenario earthquake and tornado.

The process for computing the structural damage is similar to other parts of the built environment. First, a fragility is obtained based on the hazard type and attributes of the building. Based on the fragility, the hazard intensity at the location of the building is computed. Using this information, the probability of exceeding each limit state is computed, along with the probability of damage. For the case of an earthquake hazard, soil information can be used to modify the damage probabilities to include damage due to liquefaction.

The outputs of this analysis are CSV file with probabilities of damage and JSON file with information about hazard and fragilities.

Input parameters

key name	type	name	description
result_name *	str	Result name	Name of the result dataset.
hazard_type *	str	Hazard type	Hazard type (earthquake, tsunami, tornado, hurricaneWindfields).
hazard_id *	str	Hazard id	ID of the hazard from the Hazard service.
fragility_key	str	Fragility key	Fragility key used in mapping dataset.
use_liquefaction	bool	Liquefaction	Use liquefaction, if applicable to the hazard. Default is False.
use_hazard_uncertainty	bool	Uncertainty	Use hazard uncertainty. Default is False.
num_cpu	int	Number of CPUs	Number of CPUs used for parallel computation. Default is 1.

Input datasets

key name	type	name	description
buildings *	ergo:buildingInventoryVer4 ergo:buildingInventoryVer5 ergo:buildingInventoryVer6 ergo:buildingInventoryVer7	Building dataset	A building dataset.
dfr3_mapping_set *	incore:dfr3MappingSet	DFR3 Mapping Set	DFR3 Mapping Set.

Output datasets

key name	type	parent key	name	description
ds_result *	ergo:buildingDamageVer5	buildings	Results	A dataset containing results (format: CSV).
damage_result *	incore:buildingDamageSupplement	buildings	Results	Information about applied hazard value and fragility (format: JSON).

(* required)

# import building damage analysis class
from pyincore.analyses.buildingdamage import BuildingDamage

3.1 Damage analysis with earthquake

# Define the variables to be used for input parameters

hazard_type = "earthquake"
hazard_id = "5b902cb273c3371e1236b36b"

result_name = "memphis_eq_bldg_dmg_result"

# Default Building Fragility mapping
mapping_id = "5b47b350337d4a3629076f2c"

# create fragility mapping object with id
eq_mapping_set = MappingSet(fragility_services.get_mapping(mapping_id))

# Create building damage analysis object
eq_bldg_dmg = BuildingDamage(client)

# Load local dataset (building inventory)
eq_bldg_dmg.set_input_dataset("buildings", local_buildings)

# Load fragility mapping
eq_bldg_dmg.set_input_dataset("dfr3_mapping_set", eq_mapping_set)

eq_bldg_dmg.set_parameter("result_name", result_name)
eq_bldg_dmg.set_parameter("hazard_type", hazard_type)
eq_bldg_dmg.set_parameter("hazard_id", hazard_id)
eq_bldg_dmg.set_parameter("num_cpu", 1)

True

# Run building damage analysis
eq_bldg_dmg.run_analysis()

True

# more viz will be in next secsion
eq_bldg_dmg_table = eq_bldg_dmg.get_output_dataset("ds_result").get_dataframe_from_csv()
eq_bldg_dmg_table.head()

	guid	LS_0	LS_1	LS_2	DS_0	DS_1	DS_2	DS_3
0	9e306063-02db-4c2e-ad72-d9da2287cdb0	0.848149	0.327322	2.722964e-02	0.151851	0.520827	0.300092	2.722964e-02
1	a4e29116-65d5-4f2a-8be8-92f6910f2454	0.844343	0.328299	2.860543e-02	0.155657	0.516043	0.299694	2.860543e-02
2	16e1983d-43f9-4e0b-ba59-6ec0f108fba0	0.896774	0.480925	8.756720e-02	0.103226	0.415849	0.393358	8.756720e-02
3	1b1c4c94-0c31-405d-9df5-84257debd204	0.828098	0.293753	2.738378e-02	0.171902	0.534345	0.266369	2.738378e-02
4	dd7d8779-1f48-450f-9c27-3ab9d986c626	0.970343	0.154677	1.000000e-10	0.029657	0.815666	0.154677	1.000000e-10

3.2 Damage analysis with tornado

# Use tornado from IN-CORE Hazard Services
hazard_id = "60a44ae8605f0462bd4263ac"

# visualize the tornado using pyincore-viz package
from pyincore_viz.geoutil import GeoUtil as viz
viz.plot_tornado(hazard_id, client, basemap=True)

Dataset already exists locally. Reading from local cached zip.
Unzipped folder found in the local cache. Reading from it...

hazard_type = "tornado"
hazard_id = "60a44ae8605f0462bd4263ac"
result_name = "memphis_tornado_bldg_dmg_result"
tornado_mapping_id = "5d8942dfb9219c068ea795ea" # 19 archetype tornado mapping

# create fragility mapping object with id
tornado_mapping_set = MappingSet(fragility_services.get_mapping(tornado_mapping_id))

tornado_bldg_dmg = BuildingDamage(client)
tornado_bldg_dmg.set_input_dataset("buildings", local_buildings)

tornado_bldg_dmg.set_input_dataset('dfr3_mapping_set', tornado_mapping_set)

tornado_bldg_dmg.set_parameter("result_name", result_name)
tornado_bldg_dmg.set_parameter("hazard_type", hazard_type)
tornado_bldg_dmg.set_parameter("hazard_id", hazard_id)
tornado_bldg_dmg.set_parameter("num_cpu", 1)
tornado_bldg_dmg.run_analysis()

True

# more viz will be in next secsion
tornado_bldg_dmg_table = tornado_bldg_dmg.get_output_dataset("ds_result").get_dataframe_from_csv()
tornado_bldg_dmg_table.head()

	guid	LS_0	LS_1	LS_2	DS_0	DS_1	DS_2	DS_3
0	9e306063-02db-4c2e-ad72-d9da2287cdb0	0.000000	0.000000e+00	0.0	1.000000	0.000000	0.000000e+00	0.0
1	a4e29116-65d5-4f2a-8be8-92f6910f2454	0.000000	0.000000e+00	0.0	1.000000	0.000000	0.000000e+00	0.0
2	16e1983d-43f9-4e0b-ba59-6ec0f108fba0	0.051066	2.400000e-09	0.0	0.948934	0.051066	2.400000e-09	0.0
3	1b1c4c94-0c31-405d-9df5-84257debd204	0.000000	0.000000e+00	0.0	1.000000	0.000000	0.000000e+00	0.0
4	dd7d8779-1f48-450f-9c27-3ab9d986c626	0.000000	0.000000e+00	0.0	1.000000	0.000000	0.000000e+00	0.0

4. Electric Power Facility Damage Analysis

This analysis computes electric power facility damage based on a particular hazard. In this secion, you will perform Electric Power Facility damage anlayis for both scenario earthquake and tornado

The process for computing the structural damage is similar to other parts of the built environment. First, a fragility is obtained based on the hazard type and attributes of the electric power facility. Based on the fragility, the hazard intensity at the location of the electric power facility is computed. Using this information, the probability of exceeding each limit state is computed, along with the probability of damage. For the case of an earthquake hazard, soil information can be used to modify the damage probabilities to include damage due to liquefaction.

The outputs of this analysis are CSV file with probabilities of damage and JSON file with information about hazard and fragilities.

Input parameters

key name	type	name	description
result_name *	str	Result name	Name of the result dataset.
hazard_type *	str	Hazard type	Hazard type (earthquake, tsunami, tornado, hurricaneWindfields).
hazard_id *	str	Hazard id	ID of the hazard from the Hazard service.
fragility_key	str	Fragility key	Fragility key used in mapping dataset.
use_liquefaction	bool	Liquefaction	Use liquefaction, if applicable to the hazard. Default is False.
use_hazard_uncertainty	bool	Uncertainty	Use hazard uncertainty. Default is False.
num_cpu	int	Number of CPUs	Number of CPUs used for parallel computations. Default 1.

Input datasets

key name	type	name	description
epfs *	incore:epf ergo:epf	Electric power dataset	An electric power facility dataset.
dfr3_mapping_set *	incore:dfr3MappingSet	DFR3 Mapping Set	DFR3 Mapping Set.

Output datasets

key name	type	parent key	name	description
result *	incore:epfDamageVer2	epfs	Results	A dataset containing results (format: CSV).
damage_result *	epfDamageSupplement	epfs	Results	Information about applied hazard value and fragility (format: JSON).

(* required)

# import EPF damage class
from pyincore.analyses.epfdamage import EpfDamage

3.1 Damage analysis with earthquake

# Seaside earthquake AtkinsonBoore1995
hazard_type = "earthquake"
hazard_id = "5d3b6a31b9219cf53284c73d"

# Seaside Electric power facility dataset; use remote dataset from INCORE services
epf_dataset_id = "5d263f08b9219cf93c056c68"

# Earthquake epf mapping
mapping_id = "5d489aa1b9219c0689f1988e"

result_name = "hazus_epf_dmg_result"

eq_epf_dmg = EpfDamage(client)
eq_epf_dmg.load_remote_input_dataset("epfs", epf_dataset_id)

# Load fragility mapping
epf_eq_mapping_set = MappingSet(fragility_services.get_mapping(mapping_id))
eq_epf_dmg.set_input_dataset("dfr3_mapping_set", epf_eq_mapping_set)

eq_epf_dmg.set_parameter("result_name", result_name)
eq_epf_dmg.set_parameter("hazard_type", hazard_type)
eq_epf_dmg.set_parameter("hazard_id", hazard_id)
eq_epf_dmg.set_parameter("num_cpu", 1)

# Run Analysis
eq_epf_dmg.run_analysis()

Dataset already exists locally. Reading from local cached zip.
Unzipped folder found in the local cache. Reading from it...

True

# more viz will be in next secsion
eq_epf_bldg_dmg_table = eq_epf_dmg.get_output_dataset("result").get_dataframe_from_csv()
eq_epf_bldg_dmg_table.head()

	guid	LS_0	LS_1	LS_2	LS_3	DS_0	DS_1	DS_2	DS_3	DS_4
0	c34c4207-d1e8-47d1-b29e-1ba11d951bce	0.999989	0.999982	0.999997	0.994662	0.005338	0.0	0.0	5.334768e-03	0.994662
1	402388b9-f14c-4402-9ad9-8b0965de0937	1.000000	1.000000	1.000000	1.000000	0.000000	0.0	0.0	4.800000e-08	1.000000
2	1d244334-1aef-4ced-b5f5-6f886a9d0db6	1.000000	1.000000	1.000000	1.000000	0.000000	0.0	0.0	2.700000e-09	1.000000
3	5a051ea9-a0b3-45ee-a283-82512e7b7cb6	1.000000	1.000000	1.000000	1.000000	0.000000	0.0	0.0	2.300000e-09	1.000000
4	51a54596-597b-46f9-8c8b-bc58f1105853	1.000000	1.000000	1.000000	1.000000	0.000000	0.0	0.0	2.500000e-09	1.000000

3.2 Damage analysis with tornado

hazard_type = "tornado"
hazard_id = "5d07cbeab9219c065b080930"  # The simulated EF-5 tornado shows geographical locations and the range
                                        # of wind speed of tornado hazard in Joplin.

# visualize the tornado using pyincore-viz package
viz.plot_tornado(hazard_id, client, basemap=True)

Dataset already exists locally. Reading from local cached zip.
Unzipped folder found in the local cache. Reading from it...

hazard_type = "tornado"
hazard_id = "5d07cbeab9219c065b080930"  # The simulated EF-5 tornado shows geographical locations and the range
                                        # of wind speed of tornado hazard in Joplin.
    
epf_substations_id = "5d92355bb9219c06ae7e386a"
mapping_id = "5d8a326fb9219c068ea798e7"  # Create a mapping to assign tornado fragilities to substations.
result_name = "Joplin_epf_substations_dmg_result"

tornado_epf_mapping_set = MappingSet(fragility_services.get_mapping(mapping_id))
epf_sub_dmg = EpfDamage(client)
epf_sub_dmg.load_remote_input_dataset("epfs", epf_substations_id)
epf_sub_dmg.set_input_dataset("dfr3_mapping_set", tornado_epf_mapping_set)
epf_sub_dmg.set_parameter("result_name", result_name)
epf_sub_dmg.set_parameter("hazard_type", hazard_type)
epf_sub_dmg.set_parameter("hazard_id", hazard_id)
epf_sub_dmg.set_parameter("num_cpu", 4)
epf_sub_dmg.set_parameter("fragility_key", "substations")
epf_sub_dmg.run_analysis()  # Run the EPF damage module to get substations damage results for Joplin in a .csv file format. The
                            # substations damage results herein are referred to fragilities at four damage states (insignificant,
                            # moderate, extensive, complete) for 18 substations located in Joplin that fall within the tornado
                            # path or not.

Dataset already exists locally. Reading from local cached zip.
Unzipped folder found in the local cache. Reading from it...

True

# more viz will be in next secsion
substation_dmg_result = epf_sub_dmg.get_output_dataset('result')
df_sub_dmg = substation_dmg_result.get_dataframe_from_csv()
df_sub_dmg.head()

	guid	LS_0	LS_1	LS_2	LS_3	DS_0	DS_1	DS_2	DS_3	DS_4
0	7f426763-81c6-4cac-b98d-c37a5e439b90	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
1	fc8add00-6760-4752-b1fb-7d6ca813f535	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
2	e8f4939d-763f-4743-bc61-c23e0b8e0f7a	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
3	2cab48ff-c5ce-495e-9662-67c9a177efc4	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
4	eb4ce929-5164-4ebf-ace2-708096d89db5	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0

5. Combine/compare damage output with different hazard types

comparison = eq_bldg_dmg_table.merge(tornado_bldg_dmg_table, how="outer", on="guid", suffixes=('_eq', '_tornado'))
comparison.head()

	guid	LS_0_eq	LS_1_eq	LS_2_eq	DS_0_eq	DS_1_eq	DS_2_eq	DS_3_eq	LS_0_tornado	LS_1_tornado	LS_2_tornado	DS_0_tornado	DS_1_tornado	DS_2_tornado	DS_3_tornado
0	9e306063-02db-4c2e-ad72-d9da2287cdb0	0.848149	0.327322	2.722964e-02	0.151851	0.520827	0.300092	2.722964e-02	0.000000	0.000000e+00	0.0	1.000000	0.000000	0.000000e+00	0.0
1	a4e29116-65d5-4f2a-8be8-92f6910f2454	0.844343	0.328299	2.860543e-02	0.155657	0.516043	0.299694	2.860543e-02	0.000000	0.000000e+00	0.0	1.000000	0.000000	0.000000e+00	0.0
2	16e1983d-43f9-4e0b-ba59-6ec0f108fba0	0.896774	0.480925	8.756720e-02	0.103226	0.415849	0.393358	8.756720e-02	0.051066	2.400000e-09	0.0	0.948934	0.051066	2.400000e-09	0.0
3	1b1c4c94-0c31-405d-9df5-84257debd204	0.828098	0.293753	2.738378e-02	0.171902	0.534345	0.266369	2.738378e-02	0.000000	0.000000e+00	0.0	1.000000	0.000000	0.000000e+00	0.0
4	dd7d8779-1f48-450f-9c27-3ab9d986c626	0.970343	0.154677	1.000000e-10	0.029657	0.815666	0.154677	1.000000e-10	0.000000	0.000000e+00	0.0	1.000000	0.000000	0.000000e+00	0.0

Hands-on Assignment

Please open session3-assignment.ipynb